A Visual-Based Approach for Indoor Radio Map Construction Using Smartphones.

A radio map is a collection of fingerprints and collected location information. To build more accurate Wi-Fi positioning system (WPS), a more precise radio map should be prepared in advance. Despite GPS signals are usually unreachable in most indoor venues, GPS signals are commonly used as reference locations to tag the collected fingerprints. As a result, WPS has not been able to achieve high performance in accuracy indoors, even if the place is rich in Wi-Fi signals. This paper introduces a novel method to construct a radio map without any use of GPS signals. Users' home or office address is used instead to tag the locations of collected Wi-Fi signals. Since the method utilizes Wi-Fi signals collected indoors, a more precise radio map construction is possible. For evaluation, we tested the method at four deliberately selected residential and downtown areas in Daejeon and Seoul, Korea. Once the data collection rate rises above 50%, the average error distance was within 10m or less. This indicates that address-based crowdsourcing radio map construction can be an effective means to construct global-scale but precise radio maps at low cost.

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

Recently, indoor localisation techniques that use wireless local area network (WLAN) be a-con signals have gained much attention by the research communities. Many localisations methods are used to estimate the user of mobile device in indoor environments. However, the accuracy of these methods is affected by the nature of the test-bed environment. In this paper, we introduce an experimental test-bed in a typical indoor environment. We used a finger printing-based localisation algorithm to estimate the user location. The fingerprinting technique consists of two phases: offline phase and online phase. In the offline phase, calibration points are collected at certain places in floor to build a radio map. In the online phase, deterministic and probabilistic approaches are applied in order to get the correct estimated position of a mobile device. In deterministic approach, the position of mobile device estimated by K-nearest neighbour (KNN). In probabilistic approach, the position of mobile device estimated by Bayesian network (BN). Clustering technique is proposed to improve the system's accuracy and reduce the radio map size in the offline phase. We present experimental results that improved the system accuracy and reduce the size of radio map by using the proposed clustering technique.

A Wi-Fi fingerprint consists of received signal strength values at a particular location along with the location information. A Wi-Fi radio map is constructed by maintaining a database of Wi-Fi fingerprints at various points in a building. A city radio map is a collection of radio maps for most buildings in a city. A highly precise citywide indoor positioning service is possible, if an accurate city radio map is available. However, tremendous time and effort are required to construct a city radio map through manual calibration. This paper proposes a method for constructing a city radio map through the crowdsourcing of Wi-Fi fingerprints from numerous smartphones. The proposed method classifies the buildings in a city into three categories: buildings in residential areas, commercial areas, and public areas. Then, it develops location-labeling techniques appropriate for the collected fingerprints from buildings in each category. Experiments conducted in the cities of Daejeon and Seoul revealed that the proposed method can construct a precise city radio map with minimal cost in a short period of time. Once city radio maps are constructed for most cities around the world, the global indoor positioning system will be completed.

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.

Survey-based indoor positioning systems are expensive to establish and maintain. We describe how a crowdsourced WiFi radio map building problem can be framed as a trajectory alignment problem and further solved by the existing graph-based Simultaneous Localisation and Mapping (SLAM) framework. Specifically, we show how to exploit crowd-sourced WiFi signals and construct opportunistic constraints that satisfy the WiFi signal consistency modelled by Gaussian Processes (GP). We repurpose GraphSLAM to optimise these WiFi constraints collectively to approximate an optimised global signal consistency. We implement an automatic pipeline we call GPSLAM to produce radio maps based only on crowdsourced mobile data. Evaluation based on realistically crowdsourced mobile data generated naturally and passively by common visitors of multiple shopping malls demonstrates that GPSLAM can build radio maps that achieve comparable results to laborious manual surveys while the human intervention involved is minimised.

As a widely established and accessible infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization for both mobile and stationary users. However, WLANs present several challenges that must be fulfilled to achieve localization based on Wi-Fi signals and to obtain proper coverage prediction maps. This paper presents a study based on the application of extra-trees regression (ETR) for indoor localization using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected signal strength indicator (RSSI) values collected by a mobile robot. Our methodology consists of utilizing the RSSI collected values to construct the REM, which is then leveraged to create a dataset for indoor localization. This process involves tracking a user’s movements within a specific area of interest while considering a single access point. The proposed scheme explores various machine learning (ML) regression algorithms, with hyperparameter tuning carried out to optimize their performance through 10-fold cross-validation. To assess the REM, we employed metrics, such as the root mean square error, absolute error, and R-squared error. Additionally, we evaluated the indoor localization accuracy using location error metrics. Among the ML techniques assessed, our proposed ETR-based approach demonstrates the highest performance based on these error metrics. The combination of generating coverage maps and utilizing regression techniques for localization presents a potent approach for analyzing the radio frequency environment in indoor spaces.

Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%.

In order to meet people’s diverse, complex, and changeable living requirements and aesthetic requirements, it is necessary to rationalize the design of indoor three-dimensional space. The optimization of the indoor environmental space layout is affected by the energy consumption and rationality of functional areas, resulting in indoor environmental space The performance of the layout optimization system deteriorates. In order to improve the performance of the indoor environment spatial layout and meet the living needs of more people, this paper proposes an optimal design of a spatial layout based on multi-intelligence decision-making. The research results of the article show that (1) the average confidence and the average recognition time of logo set B are both smaller than those of logo set A , indicating that logo set B is easier to be recognized by users in the same commercial building environment and can be more quickly recognized. In terms of discrimination, the time used to correct the misrecognition of identification set B is shorter than that of identification set A , which means that the different types of identifications in identification set B are easier to distinguish. (2) The spatial positioning algorithm proposed in this paper has good reliability and practicability, high positioning accuracy, and small error for indoor spatial coordinate positioning. Compared with the traditional method, the projection method of this paper has a significantly smaller error, and the maximum error is only 0.272, which makes the indoor space design more rational. (3) Comparing the energy consumption coefficients of the three spatial layout optimization systems, it can be seen that the indoor environmental spatial layout optimization system based on multi-intelligence decision-making has the lowest energy consumption when optimizing the indoor environmental spatial layout, because the system is in the design process. The indoor environment space layout model is designed, which effectively reduces the energy consumption of the indoor environment space layout optimization. When the indoor environment space layout optimization system based on multi-intelligence decision-making is used to optimize the indoor environment space layout, the average optimization accuracy is 98.32%, while the average indoor environment space layout optimization accuracy of the space layout optimization system with curved shading space layout optimization is 42.2% %, the layout optimization of binocular stereo vision space. When optimizing the indoor space layout, the average optimization accuracy is 70.87%.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

PurposeWith the rapid development of the indoor spaces positioning technologies such as the radio-frequency identification (RFID), Bluetooth and WI-FI, the locations of indoor spatial objects (static or moving) constitute an important foundation for a variety of applications. However, there are many challenges and limitations associated with the structuring and querying of spatial objects in indoor spaces. The purpose of this study is to address the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces. Also it addresses the related features of indoor spaces such as indoor structures, positioning technologies and others.Design/methodology/approachIn this paper, the author focuses on understanding the aspects and challenges of spatial database managements in indoor spaces. The author explains the differences between indoor spaces and outdoor spaces. Also examines the issues pertaining to indoor spaces positioning and the impact of different shapes and structures within these spaces. In addition, the author considers the varieties of spatial queries that relate specifically to indoor spaces.FindingsMost of the research on data management in indoor spaces does not consider the issues and the challenges associated with indoor positioning such as the overlapping of Wi-Fi. The future trend of the indoor spaces includes included different shapes of indoors beside the current 2D indoor spaces on which the majority of the data structures and query processing for spatial objects have focused on. The diversities of the indoor environments features such as directed floors, multi-floors cases should be considered and studied. Furthermore, indoor environments include many special queries besides the common ones queries that used in outdoor spaces such as KNN, range and temporal queries. These special queries need to be considered in data management and querying of indoor environments.Originality/valueTo the best of the author’s knowledge, this paper successfully addresses the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces.

In order to guarantee the pedestrian location accuracy for the indoor environment, an improved pedestrian location method based on the improved Mahony complementary filter algorithm is proposed by the data fusion of microinertial measurement unit (MIMU) and sEMG sensors, which are installed on the lower limbs. First, the pedestrian location method for the indoor environment is introduced by the data fusion of MIMU and surface electromyography; and then, BP neural network is applied to adjust the parameters in real time for PI controller of Mahony complementary filter algorithm, which can obtain the current heading angle for the location. Finally, the indoor environment experiments for pedestrian location are carried out. The heading angle estimating experiment result shows that the indoor heading angle for the pedestrian can be exactly calculated by the improved Mahony complementary filter algorithm. The indoor location experiment shows that the average relative location error is 2.34 m, and the average relative location error rate is 2.27%.

Wireless local area network (WLAN) fingerprinting has been extensively studied for indoor localization due to the formidable deployability of WLAN in indoor environment, but one major bottleneck of its practical implementation is the extensive calibration effort required to construct the radio map through the fingerprinting which is time consuming and labor intensive. In response to this compelling problem, we newly design the graph-based semi-supervised manifold alignment approach which relies on the concept of graph construction to construct a cost-efficient radio map with a small number of labeled fingerprints. In addition, the unlabeled user traces are also considered to compensate for the sparsity of the raw radio map as well as enhance its robustness to the environmental change. The extensive experiments conducted in an actual indoor WLAN environment demonstrate the performance improvement (by 27.48% at most with respect to the confidence probability of errors within 3 m) by our system compared with the existing ones using the fingerprints solely.

Since it is hard to use GPS for tracking mobile user in indoor environments, much research has focused on techniques using existing wireless local area network infrastructure. Signal strength received at a fixed location is not constant, so fingerprinting approach which use pattern matching is popular. But this approach has to pay additional costs to determine user location. This paper proposes a new approach to find user's location efficiently using an index scheme. After analyzing characteristics of RF signals, the paper suggests the data processing method how the signal strength values for each of the access points are recorded in a radio map. To reduce computational cost during the location determination phase, multidimensional indexes for radio map with the important information which is the order of the strongest access points are used.